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Our research focuses on autonomous agents and intelligent robots that plan and act under uncertainty to accomplish complex tasks. We are particularly interested in aspects relating to reliability and generalizability of methods for computing the behavior of autonomous agents, going from theoretical formulations to executable systems. Our methods draw upon formal foundations of mathematical logic, probability theory, machine learning, and well-founded notions of state and action abstractions.

Research

Generalized Planning

"Be wise, generalize!"

Planning is well known to be a hard problem. We are developing methods for acquiring useful knowledge while computing plans for small problem instances. This knowledge is then used to aid planning in larger, more difficult problems.

Often, our approaches can extract algorithmic, generalized plans that solve efficiently large classes of similar problems as well as problems with uncertainty in the quantities of objects that the agent needs to work with. The generalized plans we compute are easier to understand and are generated with proofs of correctness.

PR2 does the laundry using a generalized planner with our integrated task and motion planning system.

Planning and Reasoning Under Uncertainty

A real robot never has perfect sensors or actuators. Instead, an intelligent robot needs to be able to solve the tasks assigned to it while handling uncertainty about the environment as well as about the effects of its own actions. This is a challenging computational problem, but also one that humans solve on a routine basis (we don't have perfect sensors or actuators either!).

We are developing new methods for efficiently expressing and solving problems where the agent has limited, incomplete information about the quantities and identities of the objects that it may encounter.

POMDP Example
Open-universe tiger POMDP with an unknown number of moving tigers.

JEDAI: An Educational System for AI Planning and Reasoning

The objective of this project is to introduce AI planning concepts using mobile manipulator robots. It uses a visual programming interface to make these concepts easier to grasp. Users can get the robot to accomplish desired tasks by dynamically populating puzzle shaped blocks encoding the robot's possible actions. This allows users to carry out navigation, planning and manipulation by connecting blocks instead of writing code. AI explanation techniques are used to inform a user if their plan to achieve a particular goal fails. This helps them better grasp the fundamentals of AI planning.

Get JEDAI

The JEDAI system (JEDAI Explains Decision-Making Artificial Intelligence) in action.

Autonomous Agents That Are Easy to Understand and Safe to Work With

AI systems have the potential to improve our society in many walks of life. However, today’s AI systems require highly trained experts for their customization, configuration, and repair. This not only makes it difficult to realize the potential benefits of AI in society, but also creates large uncertainties in the future of employment for millions in the workforce.

To address these issues, we are developing new paradigms for computing user-aligned explanations of AI behavior. We are also developing well-defined AI systems that can talk to arbitrary, black-box AIs and derive a user-interpretable specification of the limits and capabilities of safe operation of the black-box AI. These methods facilitate spontaneous, productive teamwork between AI systems and people who may be experts in fields other than AI.

Capability Estimation Assistive Planning
Examples of capability estimation and explanation.

Synthesis and Analysis of Abstractions for Autonomy

In order to solve complex, long-horizon tasks such as doing the laundry, a robot needs to compute high-level strategies (e.g., would it be useful to put all the dirty clothes in a basket first?) as well as the joint movements that it should execute. Unfortunately, approaches for high-level planning rely on task-planning abstractions that are lossy and can produce “solutions” that have no feasible executions.

We are developing new methods for computing safe task-planning abstractions and for dynamically refining the task-planning abstraction to produce combined task and motion plans that are guaranteed to be executable. We are also working on utilizing abstractions in sequential decision making (SDM) for evaluating the effect of abstractions on models for SDM, as well as to search for abstractions that would aid in solving a given SDM problem.

Maze Abstraction into Rooms
Critical region (green) labelling for one sample environment. These regions can be used as waypoint abstractions.
YuMi robot builds a 3π structure with Keva planks using our STAMP algorithm.
YuMi robot builds a twisted tower with Keva planks using our STAMP algorithm.
PR2 robot sets the table for dinner using our integrated task and motion planning system.
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People



Siddharth Srivastava

Siddharth Srivastava

Assistant Professor
Director, AAIR Lab




Mehdi Dadvar

Mehdi Dadvar

Ph.D. Student

Rushang Karia

Rushang Karia

Ph.D. Student

. Rashmeet K Nayyar

Rashmeet K Nayyar

Ph.D. Student




Naman P Shah

Naman P Shah

Ph.D. Student

Pulkit Verma

Pulkit Verma

Ph.D. Student

Trevor Angle

Trevor Angle

M.S. Student




Daniel Bramblett

Daniel Bramblett

M.S. Student

Kiran Prasad

Kiran Prasad

M.S. Student

Kyle Joseph Atkinson

Kyle Joseph Atkinson

B.S. Student




Ayan Joshi

Ayan Joshi

B.S. Student

Alfred

Alfred

Robot

YuMi

YuMi

Robot




Alumni


Shashank Rao Marpally M.S. Jan 2020 - May 2021
Deepak Kala Vasudevan M.S. Aug 2019 - Dec 2020
Abhyudaya Srinet M.S. Aug 2019 - Aug 2020
Kislay Kumar M.S. May 2018 - Dec 2019
Chirav Dave M.S. Dec 2017 - Dec 2019
Daniel Molina M.S. Aug 2017 - May 2019
Midhun P. M. M.C.S. Dec 2017 - Dec 2018
Julia Nakhleh B.S. Aug 2018 - May 2019
Ryan Christensen B.S. Aug 2017 - May 2018
Perry Wang B.S. Aug 2017 - May 2018

Summer 2020

Older Group Photos

Publications


Show by area


  • Platform-Independent Benchmarks for Task and Motion Planning.
    Fabien Lagriffoul, Neil T. Dantam, Caelan Garrett, Aliakbar Akbari, Siddharth Srivastava, Lydia E. Kavraki.
    IEEE Robotics and Automation Letters (RA-L), Vol. 3, Issue 4, pp. 3765-3772, 2018.
    Mobile Manipulation State/Action Abstractions
  • Tractability of Planning with Loops.
    Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell.
    In Proceedings of AAAI, 2015.
    Partial Observability Learning State/Action Abstractions Generalized Planning Plan Generalization and Transfer
  • First-Order Open-Universe POMDPs.
    Siddharth Srivastava, Stuart Russell, Paul Ruan, Xiang Cheng.
    In Proceedings of the Conference on Uncertainty in AI (UAI), 2014.
    Partial Observability Probabilistic Inference
  • Qualitative Numeric Planning.
    Siddharth Srivastava, Shlomo Zilberstein, Neil Immerman, Hector Geffner.
    In Proceedings of the Twenty Fifth Conference on AI (AAAI), 2011.
    Partial Observability Generalized Planning State/Action Abstractions Plan Generalization and Transfer
  • Computing Applicability Conditions for Plans with Loops. [Best Paper Award] (TechReport with more results and detailed proofs)
    Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
    In Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS), 2010.
    Generalized Planning State/Action Abstractions Plan Generalization and Transfer
  • Finding Plans with Branches, Loops and Preconditions.
    Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
    ICAPS 2009 Workshop on Verification and Validation of Planning and Scheduling Systems, 2009. [slides]
    Generalized Planning State/Action Abstractions Plan Generalization and Transfer
  • Challenges in Finding Generalized Plans.
    Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
    ICAPS 2009 Workshop on Generalized Planning: Macros, Loops, Domain Control, 2009. [slides]
    Generalized Planning State/Action Abstractions Plan Generalization and Transfer
  • Using Abstraction for Generalized Planning.
    Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
    International Symposium on AI and Mathematics (ISAIM), 2008.
    State/Action Abstractions Plan Generalization and Transfer Generalized Planning

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